{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:11:07Z","timestamp":1779894667388,"version":"3.53.1"},"reference-count":21,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T00:00:00Z","timestamp":1762473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swedish State","award":["70380"],"award-info":[{"award-number":["70380"]}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health and Care Research","doi-asserted-by":"crossref","award":["RP-2-16-07-001"],"award-info":[{"award-number":["RP-2-16-07-001"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Wellcome\/EPSRC Centre for Medical Engineering at King\u2019s College London","award":["WT203148\/Z\/16\/Z"],"award-info":[{"award-number":["WT203148\/Z\/16\/Z"]}]},{"name":"NIHR Biomedical Research Centre at Guy\u2019s"},{"DOI":"10.13039\/501100004941","name":"St Thomas\u2019 NHS Foundation Trust","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004941","id-type":"DOI","asserted-by":"crossref"}]},{"name":"King\u2019s College London"},{"name":"NIHR Clinical Research Facility"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Hematology Reports"],"abstract":"<jats:p>Background: The aim was to evaluate whether an artificial intelligence (AI)-based tool for the automated quantification of the total metabolic tumour volume (tMTV) in patients with Hodgkin lymphoma (HL) could support nuclear medicine specialists in lesion segmentation and thereby enhance inter-observer agreement. Methods: Forty-eight consecutive patients who underwent staging with [18F]FDG PET\/CT were included. Eight invited specialists from different hospitals were asked to manually segment lesions for tMTV calculations in 12 cases without AI advice, and to use automated AI segmentation in a further 12 cases, with editing as required, i.e., segmenting\/adjusting 24 cases each. Each case was segmented by two specialists manually and by two different specialists using the AI tool, allowing for the pairwise comparison of inter-observer variability. Results: The median difference between two specialists performing manual tMTV segmentations was 26 cm3 (IQR 10\u201386 cm3) corresponding to 23% (IQR 7\u201350%) of the median tMTV in the dataset, while the median difference between two specialists tMTV adjustments using AI segmentations was 12 cm3 (IQR 4\u201339 cm3) corresponding to 9% (IQR 2\u201321%) (p = 0.023). The median difference in tMTV between measurements with and without AI was 3.3 cm3, corresponding to 2.3% of the median tMTV. Conclusions: An automated AI-based tool can significantly increase agreement among specialists quantifying tMTV in HL patients staged with [18F]FDG PET\/CT, without markedly changing the measurements.<\/jats:p>","DOI":"10.3390\/hematolrep17060060","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T10:56:45Z","timestamp":1762513005000},"page":"60","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI Improves Agreement and Reduces Time for Quantifying Metabolic Tumour Burden in Hodgkin Lymphoma"],"prefix":"10.3390","volume":"17","author":[{"given":"May","family":"Sadik","sequence":"first","affiliation":[{"name":"Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sally F.","family":"Barrington","sequence":"additional","affiliation":[{"name":"Clinical PET Centre, School of Biomedical Engineering and Imaging Sciences Kings College, London SE5 9RS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Ul\u00e9n","sequence":"additional","affiliation":[{"name":"Eigenvision AB, 211 30 Malm\u00f6, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olof","family":"Enqvist","sequence":"additional","affiliation":[{"name":"Eigenvision AB, 211 30 Malm\u00f6, Sweden"},{"name":"Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7116-303X","authenticated-orcid":false,"given":"Elin","family":"Tr\u00e4g\u00e5rdh","sequence":"additional","affiliation":[{"name":"Clinical Physiology and Nuclear Medicine, Lund University and Sk\u00e5ne University Hospital, 205 02 Malm\u00f6, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8632-6292","authenticated-orcid":false,"given":"Babak","family":"Saboury","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Lerberg Nielsen","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Odense University Hospital, 5000 Odense, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annika","family":"Loft","sequence":"additional","affiliation":[{"name":"Department of Clinical Physiology, Nuclear Medicine and PET, Centre of Diagnostic Investigations, Rigshospitalet, University of Copenhagen, 2100 Copenhagen, Denmark"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose Luis","family":"Loaiza Gongora","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Imaging, Akershus University Hospital, 1478 Oslo, Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesus","family":"Lopez Urdaneta","sequence":"additional","affiliation":[{"name":"Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajender","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martijn","family":"van Essen","sequence":"additional","affiliation":[{"name":"Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0263-8820","authenticated-orcid":false,"given":"Lars","family":"Edenbrandt","sequence":"additional","affiliation":[{"name":"Department of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.2967\/jnumed.119.227249","article-title":"Time to Prepare for Risk Adaptation in Lymphoma by Standardizing Measurement of Metabolic Tumor Burden","volume":"60","author":"Barrington","year":"2019","journal-title":"J. Nucl. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Weisman, A.J., Kieler, M.W., Perlman, S., Hutchings, M., Jeraj, R., Kostakoglu, L., and Bradshaw, T.J. (2020). Comparison of 11 automated PET segmentation methods in lymphoma. Phys. Med. Biol., 65.","DOI":"10.1088\/1361-6560\/abb6bd"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"332","DOI":"10.2967\/jnumed.119.238923","article-title":"Automated Segmentation of Baseline Metabolic Total Tumor Burden in Diffuse Large B-Cell Lymphoma: Which Method Is Most Successful? A Study on Behalf of the PETRA Consortium","volume":"62","author":"Barrington","year":"2021","journal-title":"J. Nucl. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1053\/j.semnuclmed.2022.10.003","article-title":"Molecular Imaging of Lymphoma: Future Directions and Perspectives","volume":"53","author":"Pomykala","year":"2023","journal-title":"Semin. Nucl. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1111\/cpf.12868","article-title":"Metabolic tumour volume in Hodgkin lymphoma-A comparison between manual and AI-based analysis","volume":"44","author":"Sadik","year":"2024","journal-title":"Clin. Physiol. Funct. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1148\/radiol.2019191114","article-title":"18F-FDG PET\/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks","volume":"294","author":"Sibille","year":"2020","journal-title":"Radiology"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sadik, M., L\u00f3pez-Urdaneta, J., Ul\u00e9n, J., Enqvist, O., Krupic, A., Kumar, R., Andersson, P.O., and Tr\u00e4g\u00e5rdh, E. (2021). Artificial intelligence could alert for focal skeleton\/bone marrow uptake in Hodgkin\u00b4s lymphoma patients staged with FDG-PET\/CT. Sci. Rep., 17.","DOI":"10.21203\/rs.3.rs-143352\/v1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1111\/cpf.12546","article-title":"Automated quantification of reference levels in liver and mediastinal blood pool for the Deauville therapy response classification using FDG-PET\/CT in Hodgkin and non-Hodgkin lymphomas","volume":"39","author":"Sadik","year":"2019","journal-title":"Clin. Physiol. Funct. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/s40658-020-00316-9","article-title":"RECOMIA-a cloud-based platform for artificial intelligence research in nuclear medicine and radiology","volume":"7","author":"Borrelli","year":"2020","journal-title":"EJNMMI Phys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"802","DOI":"10.2967\/jnumed.124.269271","article-title":"Measuring Total Metabolic Tumor Volume from18F-FDG PET: A Reality Check","volume":"66","author":"Boellaard","year":"2025","journal-title":"J. Nucl. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.2967\/jnumed.124.267789","article-title":"International Benchmark for Total Metabolic Tumor Volume Measurement in Baseline 18F-FDG PET\/CT of Lymphoma Patients: A Milestone Toward Clinical Implementation","volume":"65","author":"Boellaard","year":"2024","journal-title":"J. Nucl. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1007\/s12149-020-01521-3","article-title":"The contribution of metabolic parameters of FDG PET\/CT prior and during therapy of adult patients with lymphomas","volume":"34","author":"Casas","year":"2020","journal-title":"Ann. Nucl. Med."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"20210448","DOI":"10.1259\/bjr.20210448","article-title":"Total tumor burden in lymphoma\u2014An evolving strong prognostic parameter","volume":"94","author":"Meignan","year":"2021","journal-title":"Br. J. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/bjh.18016","article-title":"Pre-treatment total metabolic tumour volumes in lymphoma: Does quantity matter?","volume":"197","author":"Villa","year":"2022","journal-title":"Br. J. Haematol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3618","DOI":"10.1200\/JCO.2016.66.9440","article-title":"Baseline metabolic tumor volume predicts outcome in high-tumor-burden follicular lymphoma: A pooled analysis of three multicenter studies","volume":"34","author":"Meignan","year":"2016","journal-title":"J. Clin. Oncol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1456","DOI":"10.1182\/blood-2017-07-795476","article-title":"Prognostic value of baseline metabolic tumor volume in early-stage Hodgkin lymphoma in the standard arm of the H10 trial","volume":"131","author":"Cottereau","year":"2018","journal-title":"Blood"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1200\/JCO.21.02063","article-title":"Proposed New Dynamic Prognostic Index for Diffuse Large B-Cell Lymphoma: International Metabolic Prognostic Index","volume":"40","author":"Mikhaeel","year":"2022","journal-title":"J. Clin. Oncol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1186\/s40658-020-00346-3","article-title":"Automated quantification of baseline imaging PET metrics on FDG PET\/CT images of pediatric Hodgkin lymphoma patients","volume":"7","author":"Weisman","year":"2020","journal-title":"EJNMMI Phys."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.2967\/jnumed.116.177030","article-title":"A Preanalytic Validation Study of Automated Bone Scan Index: Effect on Accuracy and Reproducibility Due to the Procedural Variabilities in Bone Scan Image Acquisition","volume":"57","author":"Anand","year":"2016","journal-title":"J. Nucl. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"41","DOI":"10.2967\/jnumed.115.160085","article-title":"Analytic Validation of the Automated Bone Scan Index as an Imaging Biomarker to Standardize Quantitative Changes in Bone Scans of Patients with Metastatic Prostate Cancer","volume":"57","author":"Anand","year":"2016","journal-title":"J. Nucl. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1001\/jamaoncol.2018.1093","article-title":"Phase 3 Assessment of the Automated Bone Scan Index as a Prognostic Imaging Biomarker of Overall Survival in Men With Metastatic Castration-Resistant Prostate Cancer: A Secondary Analysis of a Randomized Clinical Trial","volume":"4","author":"Armstrong","year":"2018","journal-title":"JAMA Oncol."}],"container-title":["Hematology Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2038-8330\/17\/6\/60\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T05:17:41Z","timestamp":1762665461000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2038-8330\/17\/6\/60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,7]]},"references-count":21,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["hematolrep17060060"],"URL":"https:\/\/doi.org\/10.3390\/hematolrep17060060","relation":{},"ISSN":["2038-8330"],"issn-type":[{"value":"2038-8330","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,7]]}}}